The output of applying PH to a data set is often represented as a persistence diagram, that for a given dimension \(k\)  consists of a plot of points \((b,d)\) , where \(b\)  is the time of birth and \(d\)  is the time of death \(d\)  of each dimension \(k\)  topological feature in the filtration. To allow for the straightforward application of methods from machine learning to these diagrams, [1]} developed the concept of a persistence image. This allows a persistence diagram to be represented as a vector in \({R}^n\) , so that for example it can be used in methods such as K-means clustering, as in [2]}.
